A standard group is used as an indication of how a population will typically score on a scale of a questionnaire (iWAM or VSQ).
The indication is a range of typical scores. jobEQ uses this range on its feedback reports
in order to give a relative indication of where a person scores in comparison
to others. The standard group can be any group, such as a team of sales
people, all employees of a certain organization, or quite often the population
of a country.

Once we know how a group typically
scores, we can say that a person's score is low or high, relatively speaking,
compared to that particular population. For instance, an absolute score
of 69% on proactivity may be very high compared to typical scores in France,
while it will just within the range of the standard group for the U.K.
(once again, the percentages are relative scores). This person will be
seen as very proactive by the large majority of the French, while his
proactivity will be considered slightly above "average" in several
other countries.

Australia:

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The
green line indicates the score of
the individual, the red part of the
bar indicates the standard group and the blue
area is outside the standard group.

France:

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UK:

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US:

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How helpful are these groups for adding validity to the test results?

The purpose of using standard
groups is not to add statistical validity; rather standard groups help
people understand the test results by showing how individuals compare
to the rest of a population. We use them to generate visual charts and/or
textual explanations of a person's scores. When jobEQ questionnaires are
used for making decisions, these standard groups do not matter. The correct
process for making decisions is comparing a person to the top performers
holding a certain position (jobEQ's Model
of Excellence technology).

For which countries are Standard Groups available?

We have standardgroups for most countries where we have an active jobEQ partner. In most cases, the partner will have created a standardgroup of iWAM.
In some cases, there will be a VSQ standardgroup as well. For some countries, good standardgroup documentation is available on-line from this website:

Here is where it gets a little
technical. jobEQ's standard groups are calculated by taking the means
of a sample of a group (e.g. a country such as the United States), adding
one standard deviation to this means to find the upper limit of the standard
group, and subtracting the standard deviation to find the lower limit.
If we presuppose that the population is approximately normal distributed,
we know by definition that approximately two thirds of the population
will fall within that standard group, while 1 out of 6 will score higher
than the standard group and 1 out of 6 will score lower.

Many tests you see calibrate
their standard group by testing student populations. This method, however,
results in unrealistic results, so jobEQ used working-age participants
(18 to 65 years old). The test participants used for the jobEQ standard
groups have all been tested since 2000. Most completed high school, and
most a white collar workers. The populations are evenly distributed between
men and women. jobEQ continues to create standard groups for more countries
as our client list expands across the globe. Of course, existing standard groups get updated as well.
Example: Documentation of the 2007 US Standardgroup

Are these groups statistically
valid?

For those inquiring minds that want to know the statistics behind our standard groups: even for the 2002 standard groups, the error margin
was always less than 5%. For Australia, it's 3.15%, for the U.K., it's 1.16%, and for the U.S. it's only 1.06% error.
Error margins will even become better for newer standard groups, given they will include more people.
Once again, it is important to note that we use standard groups just as a guide to help understand test results,
so the key is not determining the exact numbers; instead it is most important to get a close estimate that will
illustrate how participants compare with their peers.

With these standard groups,
we get a good approximation of the standard for a culture. The example
below will help to clarify this. (note: we picked these samples for educational
purposes: these are NOT the specific standard groups used in our system.)
The statistics below
compare the scores on proactivity (parameter OF1P in the iWAM questionnaire)
between a French population (FR) from our public profiling database with
a British population (UK) from the same database. Both populations are
working populations (ages 18 to 65) and are mainly white collar workers
from different sectors (both public and private sector, from education
to consulting or from secretary to top executive).

We tested 238 persons for France
and 329 persons for the U.K. We found that these cultures are so different
that an eventual statistical error for the groups is much smaller than
the cultural difference found. Statistically speaking, the difference
between the two cultures thus is very significant (P<0.001). In other
words, there is less than one chance in thousand that the French would
be as proactive as the British.

Group Statistics

COUNTRY

N

Mean

Std. Deviation

Std. Error
Mean

OF1P

UK

329

.5651596

.20978483

.01156581

FR

238

.4280462

.15986352

.01036241

Independent Samples Test

Levene's Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

OF1P

Equal variances assumed

19.967

.000

8.461

565

.000

.1371134

.01620591

.10528217

.16894454

Equal variances not assumed

8.830

563.458

.000

.1371134

.01552893

.10661170

.16761501

If we would now repeat the same exercise for comparing U.S. and Australia, again using
data from jobEQ's public database as on May 28, 2002, we would see that the differences
for proactivity are not significant. Yet again, the statistics show that
the possible statistical error on these groups is quite small, even if
due to sample size the statistical error margin on the Australian group
is much larger (3.15%) than the error on the larger U.S. group (1.06%).
So we can safely assume that a bigger sample would allow us to come to
the same conclusions, that is: that the Americans do not differ very much
from the Australians in proactivity (nor from the British). The important
cultural differences can be seen from a group of charts
we prepared for that.

Group Statistics

COUNTRY

N

Mean

Std. Deviation

Std. Error
Mean

OF1P

AU

53

.5683962

.22939882

.03151035

US

482

.5344917

.23380502

.01064953

Independent Samples Test

Levene's Test for Equality of Variances

t-test for Equality of Means

F

Sig.

t

df

Sig. (2-tailed)

Mean Difference

Std. Error Difference

95% Confidence Interval of the Difference

Lower

Upper

OF1P

Equal variances assumed

.374

.541

1.004

533

.316

.0339045

.03377356

-.03244109

.10025014

Equal variances not assumed

1.019

64.467

.312

.0339045

.03326131

-.03253332

.10034237

Statistics generated using SPSS for Windows v.11 on an extraction of
jobEQ's Public Profiling database as of May 28, 2002.

The argument that the standard group for Australia wouldn't change much
with sample size is confirmed by this second sample from July 11, 2002, after
we started a call for Australian questionnaires. You'll notice that the change
in mean is well within the predicted standard error of the first sample,
and given this sample is a larger (n=83), the predicted error margin
is smaller.